In this paper, we establish finite-sample convergence guarantees of off-policy NAC with linear function approximation. To overcome the deadly triad in the critic, we use \(n\) -step TD-learning, which is a one-time scale algorithm for policy evaluation using off-policy sampling and linear function approximation, and has provable convergence bounds. As for the analysis of the actor, we identify an equivalent update equation, and use it to conduct refined analysis compared to [1]}. As a result, our finite-sample bounds imply a sample complexity of \(\tilde{\mathcal {O}}(\epsilon ^{-3})\) , which advances the state-of-the-art result in the literature.
